Abstract
Several ensemble-based three-dimensional variational (3D-Var) filters are compared. These schemes replace the static background error covariance of the traditional 3D-Var with the ensemble forecast error covariance, but generate analysis ensemble anomalies (perturbations) in different ways. However, it is demonstrated in this paper that they are all theoretically equivalent to the ensemble transformation Kalman filter (ETKF). Furthermore, a new method named EnPSAS is presented. The analysis shows that EnPSAS has a small condition number and can apply covariance localization more easily than other ensemble-based 3D-Var methods.
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Project supported by the National Natural Science Foundation of China (No. 41105063) and the Special Fund for Meteorological Scientific Research in the Public Interest (No. GYHY20100615)
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Leng, Hz., Song, Jq., Yin, Fk. et al. Notes and correspondence on ensemble-based three-dimensional variational filters. J. Zhejiang Univ. - Sci. C 14, 634–641 (2013). https://doi.org/10.1631/jzus.C1300024
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DOI: https://doi.org/10.1631/jzus.C1300024
Key words
- 3D-Var
- Ensemble Kalman filter (EnKF)
- Ensemble transformation Kalman filter (ETKF)
- Physical space analysis system (PSAS)
- Ensemble data assimilation